Third edition of Artificial Intelligence: foundations of computational agents, Cambridge University Press, 2023 is now available (including the full text).
1.1 What Is Artificial Intelligence?
Artificial intelligence, or AI, is the field that studies the synthesis and analysis of computational agents that act intelligently. Let us examine each part of this definition.
An agent is something that acts in an environment - it does something. Agents include worms, dogs, thermostats, airplanes, robots, humans, companies, and countries.
We are interested in what an agent does; that is, how it acts. We judge an agent by its actions.
An agent acts intelligently when
- what it does is appropriate for its circumstances and its goals,
- it is flexible to changing environments and changing goals,
- it learns from experience, and
- it makes appropriate choices given its perceptual and computational limitations. An agent typically cannot observe the state of the world directly; it has only a finite memory and it does not have unlimited time to act.
A computational agent is an agent whose decisions about its actions can be explained in terms of computation. That is, the decision can be broken down into primitive operation that can be implemented in a physical device. This computation can take many forms. In humans this computation is carried out in "wetware"; in computers it is carried out in "hardware." Although there are some agents that are arguably not computational, such as the wind and rain eroding a landscape, it is an open question whether all intelligent agents are computational.
The central scientific goal of AI is to understand the principles that make intelligent behavior possible in natural or artificial systems. This is done by
- the analysis of natural and artificial agents;
- formulating and testing hypotheses about what it takes to construct intelligent agents; and
- designing, building, and experimenting with computational systems that perform tasks commonly viewed as requiring intelligence.
As part of science, researchers build empirical systems to test hypotheses or to explore the space of possibilities. These are quite distinct from applications that are built to be useful for an application domain.
Note that the definition is not for intelligent thought. We are only interested in thinking intelligently insofar as it leads to better performance. The role of thought is to affect action.
The central engineering goal of AI is the design and synthesis of useful, intelligent artifacts. We actually want to build agents that act intelligently. Such agents are useful in many applications.
